Merck & Co., Inc., Kenilworth, NJ, United States of America.
Department of Statistics, Western Michigan University, Kalamazoo, Michigan, United States of America.
PLoS One. 2020 Feb 26;15(2):e0229336. doi: 10.1371/journal.pone.0229336. eCollection 2020.
Due to differential treatment responses of patients to pharmacotherapy, drug development and practice in medicine are concerned with personalized medicine, which includes identifying subgroups of population that exhibit differential treatment effect. For time-to-event data, available methods only focus on detecting and testing treatment-by-covariate interactions and may not consider multiplicity. In this work, we introduce the Bayesian credible subgroups approach for time-to-event endpoints. It provides two bounding subgroups for the true benefiting subgroup: one which is likely to be contained by the benefiting subgroup and one which is likely to contain the benefiting subgroup. A personalized treatment effect is estimated by two common measures of survival time: the hazard ratio and restricted mean survival time. We apply the method to identify benefiting subgroups in a case study of prostate carcinoma patients and a simulated large clinical dataset.
由于患者对药物治疗的反应存在差异，药物开发和医学实践都关注个性化医学，包括识别表现出不同治疗效果的人群亚组。对于生存时间数据，现有的方法仅侧重于检测和检验治疗与协变量的相互作用，而可能不考虑多重性。在这项工作中，我们引入了用于生存时间终点的贝叶斯可信亚组方法。它为真正受益的亚组提供了两个边界亚组：一个可能包含受益亚组，另一个可能包含受益亚组。通过两种常见的生存时间衡量标准：风险比和受限平均生存时间，估计个性化治疗效果。我们将该方法应用于前列腺癌患者的病例研究和模拟的大型临床数据集，以确定受益亚组。
